Overview

Dataset statistics

Number of variables14
Number of observations178
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.9 KiB
Average record size in memory108.7 B

Variable types

Numeric13
Categorical1

Warnings

alcohol is highly correlated with color_intensity and 1 other fieldsHigh correlation
malic_acid is highly correlated with hueHigh correlation
alcalinity_of_ash is highly correlated with targetHigh correlation
total_phenols is highly correlated with flavanoids and 3 other fieldsHigh correlation
flavanoids is highly correlated with total_phenols and 5 other fieldsHigh correlation
nonflavanoid_phenols is highly correlated with flavanoids and 1 other fieldsHigh correlation
proanthocyanins is highly correlated with total_phenols and 2 other fieldsHigh correlation
color_intensity is highly correlated with alcohol and 1 other fieldsHigh correlation
hue is highly correlated with malic_acid and 4 other fieldsHigh correlation
od280/od315_of_diluted_wines is highly correlated with total_phenols and 5 other fieldsHigh correlation
proline is highly correlated with alcohol and 1 other fieldsHigh correlation
target is highly correlated with alcalinity_of_ash and 5 other fieldsHigh correlation
alcohol is highly correlated with color_intensity and 1 other fieldsHigh correlation
malic_acid is highly correlated with hueHigh correlation
alcalinity_of_ash is highly correlated with targetHigh correlation
magnesium is highly correlated with prolineHigh correlation
total_phenols is highly correlated with flavanoids and 3 other fieldsHigh correlation
flavanoids is highly correlated with total_phenols and 5 other fieldsHigh correlation
nonflavanoid_phenols is highly correlated with flavanoidsHigh correlation
proanthocyanins is highly correlated with total_phenols and 3 other fieldsHigh correlation
color_intensity is highly correlated with alcoholHigh correlation
hue is highly correlated with malic_acid and 2 other fieldsHigh correlation
od280/od315_of_diluted_wines is highly correlated with total_phenols and 3 other fieldsHigh correlation
proline is highly correlated with alcohol and 2 other fieldsHigh correlation
target is highly correlated with alcalinity_of_ash and 6 other fieldsHigh correlation
total_phenols is highly correlated with flavanoids and 1 other fieldsHigh correlation
flavanoids is highly correlated with total_phenols and 3 other fieldsHigh correlation
proanthocyanins is highly correlated with flavanoidsHigh correlation
od280/od315_of_diluted_wines is highly correlated with flavanoids and 1 other fieldsHigh correlation
target is highly correlated with total_phenols and 2 other fieldsHigh correlation
flavanoids is highly correlated with color_intensity and 6 other fieldsHigh correlation
ash is highly correlated with alcalinity_of_ashHigh correlation
color_intensity is highly correlated with flavanoids and 4 other fieldsHigh correlation
proline is highly correlated with flavanoids and 7 other fieldsHigh correlation
total_phenols is highly correlated with flavanoids and 5 other fieldsHigh correlation
proanthocyanins is highly correlated with flavanoids and 6 other fieldsHigh correlation
hue is highly correlated with flavanoids and 3 other fieldsHigh correlation
alcohol is highly correlated with color_intensity and 4 other fieldsHigh correlation
malic_acid is highly correlated with total_phenols and 1 other fieldsHigh correlation
magnesium is highly correlated with proline and 2 other fieldsHigh correlation
alcalinity_of_ash is highly correlated with ash and 3 other fieldsHigh correlation
od280/od315_of_diluted_wines is highly correlated with flavanoids and 5 other fieldsHigh correlation
target is highly correlated with flavanoids and 11 other fieldsHigh correlation
nonflavanoid_phenols is highly correlated with od280/od315_of_diluted_wines and 1 other fieldsHigh correlation

Reproduction

Analysis started2021-08-03 19:47:21.881525
Analysis finished2021-08-03 19:47:53.018006
Duration31.14 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

alcohol
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct126
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.00061798
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:53.185846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.6585
Q112.3625
median13.05
Q313.6775
95-th percentile14.2215
Maximum14.83
Range3.8
Interquartile range (IQR)1.315

Descriptive statistics

Standard deviation0.811826538
Coefficient of variation (CV)0.06244522679
Kurtosis-0.8524995685
Mean13.00061798
Median Absolute Deviation (MAD)0.68
Skewness-0.05148233108
Sum2314.11
Variance0.6590623278
MonotonicityNot monotonic
2021-08-03T20:47:53.427973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.376
 
3.4%
13.056
 
3.4%
12.085
 
2.8%
12.294
 
2.2%
123
 
1.7%
12.253
 
1.7%
12.423
 
1.7%
12.932
 
1.1%
12.62
 
1.1%
12.852
 
1.1%
Other values (116)142
79.8%
ValueCountFrequency (%)
11.031
0.6%
11.411
0.6%
11.451
0.6%
11.461
0.6%
11.561
0.6%
11.611
0.6%
11.621
0.6%
11.641
0.6%
11.651
0.6%
11.661
0.6%
ValueCountFrequency (%)
14.831
0.6%
14.751
0.6%
14.391
0.6%
14.382
1.1%
14.371
0.6%
14.341
0.6%
14.31
0.6%
14.231
0.6%
14.222
1.1%
14.211
0.6%

malic_acid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct133
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.336348315
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:53.597067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.061
Q11.6025
median1.865
Q33.0825
95-th percentile4.4555
Maximum5.8
Range5.06
Interquartile range (IQR)1.48

Descriptive statistics

Standard deviation1.117146098
Coefficient of variation (CV)0.478159053
Kurtosis0.2992066799
Mean2.336348315
Median Absolute Deviation (MAD)0.52
Skewness1.039651193
Sum415.87
Variance1.248015403
MonotonicityNot monotonic
2021-08-03T20:47:53.773089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.737
 
3.9%
1.814
 
2.2%
1.674
 
2.2%
1.683
 
1.7%
1.613
 
1.7%
1.513
 
1.7%
1.353
 
1.7%
1.533
 
1.7%
1.93
 
1.7%
3.172
 
1.1%
Other values (123)143
80.3%
ValueCountFrequency (%)
0.741
0.6%
0.891
0.6%
0.91
0.6%
0.921
0.6%
0.942
1.1%
0.981
0.6%
0.991
0.6%
1.011
0.6%
1.071
0.6%
1.091
0.6%
ValueCountFrequency (%)
5.81
0.6%
5.651
0.6%
5.511
0.6%
5.191
0.6%
5.041
0.6%
4.951
0.6%
4.721
0.6%
4.611
0.6%
4.61
0.6%
4.431
0.6%

ash
Real number (ℝ≥0)

HIGH CORRELATION

Distinct79
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.366516854
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:53.934225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.5575
95-th percentile2.7415
Maximum3.23
Range1.87
Interquartile range (IQR)0.3475

Descriptive statistics

Standard deviation0.2743440091
Coefficient of variation (CV)0.1159273422
Kurtosis1.143978169
Mean2.366516854
Median Absolute Deviation (MAD)0.16
Skewness-0.1766993165
Sum421.24
Variance0.07526463531
MonotonicityNot monotonic
2021-08-03T20:47:54.159542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.37
 
3.9%
2.287
 
3.9%
2.76
 
3.4%
2.366
 
3.4%
2.326
 
3.4%
2.485
 
2.8%
2.25
 
2.8%
2.385
 
2.8%
2.54
 
2.2%
2.44
 
2.2%
Other values (69)123
69.1%
ValueCountFrequency (%)
1.361
 
0.6%
1.72
1.1%
1.711
 
0.6%
1.751
 
0.6%
1.821
 
0.6%
1.881
 
0.6%
1.91
 
0.6%
1.923
1.7%
1.941
 
0.6%
1.951
 
0.6%
ValueCountFrequency (%)
3.231
0.6%
3.221
0.6%
2.921
0.6%
2.871
0.6%
2.861
0.6%
2.841
0.6%
2.81
0.6%
2.781
0.6%
2.751
0.6%
2.742
1.1%

alcalinity_of_ash
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.49494382
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:54.405509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.77
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.339563767
Coefficient of variation (CV)0.171304098
Kurtosis0.4879415405
Mean19.49494382
Median Absolute Deviation (MAD)2.05
Skewness0.2130468864
Sum3470.1
Variance11.15268616
MonotonicityNot monotonic
2021-08-03T20:47:54.574162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015
 
8.4%
2111
 
6.2%
1611
 
6.2%
1810
 
5.6%
199
 
5.1%
21.58
 
4.5%
18.57
 
3.9%
227
 
3.9%
19.57
 
3.9%
22.57
 
3.9%
Other values (53)86
48.3%
ValueCountFrequency (%)
10.61
0.6%
11.21
0.6%
11.41
0.6%
121
0.6%
12.41
0.6%
13.21
0.6%
142
1.1%
14.61
0.6%
14.81
0.6%
152
1.1%
ValueCountFrequency (%)
301
 
0.6%
28.52
 
1.1%
271
 
0.6%
26.51
 
0.6%
261
 
0.6%
25.51
 
0.6%
255
2.8%
24.53
1.7%
245
2.8%
23.61
 
0.6%

magnesium
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.74157303
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:54.758301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.85
Q188
median98
Q3107
95-th percentile124.3
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.28248352
Coefficient of variation (CV)0.1431948894
Kurtosis2.104991324
Mean99.74157303
Median Absolute Deviation (MAD)10
Skewness1.098191055
Sum17754
Variance203.9893354
MonotonicityNot monotonic
2021-08-03T20:47:54.929054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8813
 
7.3%
8611
 
6.2%
989
 
5.1%
1019
 
5.1%
968
 
4.5%
1027
 
3.9%
1126
 
3.4%
856
 
3.4%
946
 
3.4%
805
 
2.8%
Other values (43)98
55.1%
ValueCountFrequency (%)
701
 
0.6%
783
 
1.7%
805
 
2.8%
811
 
0.6%
821
 
0.6%
843
 
1.7%
856
3.4%
8611
6.2%
873
 
1.7%
8813
7.3%
ValueCountFrequency (%)
1621
0.6%
1511
0.6%
1391
0.6%
1361
0.6%
1341
0.6%
1321
0.6%
1281
0.6%
1271
0.6%
1261
0.6%
1241
0.6%

total_phenols
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct97
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.29511236
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:55.106290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.7425
median2.355
Q32.8
95-th percentile3.2745
Maximum3.88
Range2.9
Interquartile range (IQR)1.0575

Descriptive statistics

Standard deviation0.6258510488
Coefficient of variation (CV)0.2726886317
Kurtosis-0.8356265234
Mean2.29511236
Median Absolute Deviation (MAD)0.505
Skewness0.0866385864
Sum408.53
Variance0.3916895353
MonotonicityNot monotonic
2021-08-03T20:47:55.307711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28
 
4.5%
36
 
3.4%
2.86
 
3.4%
2.66
 
3.4%
25
 
2.8%
2.955
 
2.8%
1.384
 
2.2%
1.654
 
2.2%
2.454
 
2.2%
2.854
 
2.2%
Other values (87)126
70.8%
ValueCountFrequency (%)
0.981
 
0.6%
1.11
 
0.6%
1.151
 
0.6%
1.251
 
0.6%
1.281
 
0.6%
1.31
 
0.6%
1.351
 
0.6%
1.384
2.2%
1.392
1.1%
1.42
1.1%
ValueCountFrequency (%)
3.881
 
0.6%
3.851
 
0.6%
3.521
 
0.6%
3.51
 
0.6%
3.41
 
0.6%
3.381
 
0.6%
3.33
1.7%
3.271
 
0.6%
3.252
1.1%
3.21
 
0.6%

flavanoids
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.029269663
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:55.916094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5455
Q11.205
median2.135
Q32.875
95-th percentile3.4975
Maximum5.08
Range4.74
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.998858685
Coefficient of variation (CV)0.4922257023
Kurtosis-0.8803815472
Mean2.029269663
Median Absolute Deviation (MAD)0.835
Skewness0.02534355338
Sum361.21
Variance0.9977186726
MonotonicityNot monotonic
2021-08-03T20:47:56.076538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.654
 
2.2%
0.583
 
1.7%
2.683
 
1.7%
0.63
 
1.7%
1.253
 
1.7%
2.033
 
1.7%
0.922
 
1.1%
0.662
 
1.1%
2.432
 
1.1%
2.982
 
1.1%
Other values (122)151
84.8%
ValueCountFrequency (%)
0.341
0.6%
0.472
1.1%
0.481
0.6%
0.491
0.6%
0.52
1.1%
0.511
0.6%
0.521
0.6%
0.551
0.6%
0.561
0.6%
0.571
0.6%
ValueCountFrequency (%)
5.081
0.6%
3.931
0.6%
3.751
0.6%
3.741
0.6%
3.691
0.6%
3.671
0.6%
3.641
0.6%
3.561
0.6%
3.541
0.6%
3.491
0.6%

nonflavanoid_phenols
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3618539326
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:56.284660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.4375
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.1244533403
Coefficient of variation (CV)0.3439325349
Kurtosis-0.6371910641
Mean0.3618539326
Median Absolute Deviation (MAD)0.085
Skewness0.4501513356
Sum64.41
Variance0.01548863391
MonotonicityNot monotonic
2021-08-03T20:47:56.433380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.2611
 
6.2%
0.4311
 
6.2%
0.2910
 
5.6%
0.329
 
5.1%
0.38
 
4.5%
0.378
 
4.5%
0.348
 
4.5%
0.278
 
4.5%
0.48
 
4.5%
0.247
 
3.9%
Other values (29)90
50.6%
ValueCountFrequency (%)
0.131
 
0.6%
0.142
 
1.1%
0.175
2.8%
0.192
 
1.1%
0.22
 
1.1%
0.216
3.4%
0.226
3.4%
0.247
3.9%
0.252
 
1.1%
0.2611
6.2%
ValueCountFrequency (%)
0.661
 
0.6%
0.634
2.2%
0.613
1.7%
0.63
1.7%
0.583
1.7%
0.561
 
0.6%
0.551
 
0.6%
0.537
3.9%
0.525
2.8%
0.55
2.8%

proanthocyanins
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct101
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.590898876
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:56.606146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.555
Q31.95
95-th percentile2.709
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.5723588627
Coefficient of variation (CV)0.3597707379
Kurtosis0.5546485226
Mean1.590898876
Median Absolute Deviation (MAD)0.38
Skewness0.5171371723
Sum283.18
Variance0.3275946677
MonotonicityNot monotonic
2021-08-03T20:47:56.777400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.359
 
5.1%
1.467
 
3.9%
1.876
 
3.4%
1.255
 
2.8%
1.564
 
2.2%
1.664
 
2.2%
1.984
 
2.2%
2.084
 
2.2%
1.773
 
1.7%
1.633
 
1.7%
Other values (91)129
72.5%
ValueCountFrequency (%)
0.411
0.6%
0.422
1.1%
0.551
0.6%
0.621
0.6%
0.642
1.1%
0.681
0.6%
0.732
1.1%
0.751
0.6%
0.82
1.1%
0.811
0.6%
ValueCountFrequency (%)
3.581
 
0.6%
3.281
 
0.6%
2.961
 
0.6%
2.912
1.1%
2.813
1.7%
2.761
 
0.6%
2.71
 
0.6%
2.51
 
0.6%
2.491
 
0.6%
2.451
 
0.6%

color_intensity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.058089882
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:56.962421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.114
Q13.22
median4.69
Q36.2
95-th percentile9.598
Maximum13
Range11.72
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation2.318285872
Coefficient of variation (CV)0.4583322807
Kurtosis0.3815222728
Mean5.058089882
Median Absolute Deviation (MAD)1.51
Skewness0.868584791
Sum900.339999
Variance5.374449383
MonotonicityNot monotonic
2021-08-03T20:47:57.180600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.64
 
2.2%
4.64
 
2.2%
3.84
 
2.2%
3.43
 
1.7%
3.053
 
1.7%
2.93
 
1.7%
53
 
1.7%
4.53
 
1.7%
5.73
 
1.7%
2.83
 
1.7%
Other values (122)145
81.5%
ValueCountFrequency (%)
1.281
0.6%
1.741
0.6%
1.91
0.6%
1.952
1.1%
21
0.6%
2.062
1.1%
2.081
0.6%
2.121
0.6%
2.151
0.6%
2.21
0.6%
ValueCountFrequency (%)
131
0.6%
11.751
0.6%
10.81
0.6%
10.681
0.6%
10.521
0.6%
10.261
0.6%
10.21
0.6%
9.8999991
0.6%
9.71
0.6%
9.581
0.6%

hue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct78
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9574494382
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:57.328001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7825
median0.965
Q31.12
95-th percentile1.2845
Maximum1.71
Range1.23
Interquartile range (IQR)0.3375

Descriptive statistics

Standard deviation0.2285715658
Coefficient of variation (CV)0.2387296464
Kurtosis-0.3440957414
Mean0.9574494382
Median Absolute Deviation (MAD)0.165
Skewness0.0210912722
Sum170.426
Variance0.05224496071
MonotonicityNot monotonic
2021-08-03T20:47:57.499927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.048
 
4.5%
1.237
 
3.9%
1.126
 
3.4%
0.895
 
2.8%
0.575
 
2.8%
0.965
 
2.8%
1.255
 
2.8%
1.054
 
2.2%
1.094
 
2.2%
0.754
 
2.2%
Other values (68)125
70.2%
ValueCountFrequency (%)
0.481
 
0.6%
0.541
 
0.6%
0.551
 
0.6%
0.562
 
1.1%
0.575
2.8%
0.582
 
1.1%
0.592
 
1.1%
0.63
1.7%
0.612
 
1.1%
0.621
 
0.6%
ValueCountFrequency (%)
1.711
 
0.6%
1.451
 
0.6%
1.421
 
0.6%
1.381
 
0.6%
1.362
 
1.1%
1.331
 
0.6%
1.312
 
1.1%
1.282
 
1.1%
1.271
 
0.6%
1.255
2.8%

od280/od315_of_diluted_wines
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct122
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.611685393
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:57.656140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4625
Q11.9375
median2.78
Q33.17
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.2325

Descriptive statistics

Standard deviation0.7099904288
Coefficient of variation (CV)0.2718514376
Kurtosis-1.086434527
Mean2.611685393
Median Absolute Deviation (MAD)0.52
Skewness-0.307285499
Sum464.88
Variance0.5040864089
MonotonicityNot monotonic
2021-08-03T20:47:57.801896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.875
 
2.8%
34
 
2.2%
1.824
 
2.2%
2.784
 
2.2%
2.773
 
1.7%
1.753
 
1.7%
1.333
 
1.7%
2.313
 
1.7%
3.333
 
1.7%
2.963
 
1.7%
Other values (112)143
80.3%
ValueCountFrequency (%)
1.271
 
0.6%
1.292
1.1%
1.31
 
0.6%
1.333
1.7%
1.361
 
0.6%
1.421
 
0.6%
1.471
 
0.6%
1.481
 
0.6%
1.512
1.1%
1.551
 
0.6%
ValueCountFrequency (%)
41
0.6%
3.921
0.6%
3.821
0.6%
3.711
0.6%
3.691
0.6%
3.641
0.6%
3.631
0.6%
3.591
0.6%
3.582
1.1%
3.571
0.6%

proline
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct121
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.8932584
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-08-03T20:47:57.988838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.55
Q1500.5
median673.5
Q3985
95-th percentile1297.25
Maximum1680
Range1402
Interquartile range (IQR)484.5

Descriptive statistics

Standard deviation314.9074743
Coefficient of variation (CV)0.4216231312
Kurtosis-0.2484031061
Mean746.8932584
Median Absolute Deviation (MAD)202.5
Skewness0.7678217814
Sum132947
Variance99166.71736
MonotonicityNot monotonic
2021-08-03T20:47:58.165066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6805
 
2.8%
5205
 
2.8%
7504
 
2.2%
6304
 
2.2%
6254
 
2.2%
4953
 
1.7%
5623
 
1.7%
4503
 
1.7%
4803
 
1.7%
6603
 
1.7%
Other values (111)141
79.2%
ValueCountFrequency (%)
2781
0.6%
2901
0.6%
3121
0.6%
3151
0.6%
3251
0.6%
3421
0.6%
3452
1.1%
3521
0.6%
3551
0.6%
3651
0.6%
ValueCountFrequency (%)
16801
0.6%
15471
0.6%
15151
0.6%
15101
0.6%
14801
0.6%
14501
0.6%
13751
0.6%
13201
0.6%
13101
0.6%
12951
0.6%

target
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1
71 
0
59 
2
48 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters178
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
171
39.9%
059
33.1%
248
27.0%

Length

2021-08-03T20:47:58.509337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-03T20:47:58.599664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
171
39.9%
059
33.1%
248
27.0%

Most occurring characters

ValueCountFrequency (%)
171
39.9%
059
33.1%
248
27.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number178
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
171
39.9%
059
33.1%
248
27.0%

Most occurring scripts

ValueCountFrequency (%)
Common178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
171
39.9%
059
33.1%
248
27.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
171
39.9%
059
33.1%
248
27.0%

Interactions

2021-08-03T20:47:25.959532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:26.586796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:26.738739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:26.898653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:27.066582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:27.220809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:27.381362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:27.525298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:27.669240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:27.827091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:27.985265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:28.157272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:28.317206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:28.469308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:28.621246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:28.749193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:28.918711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:29.084986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:29.212368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:29.348313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:29.476265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:29.604096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:29.740033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:29.876026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:30.003931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:30.131881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:30.267829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:30.433033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:30.611851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:30.795771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:30.955709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:31.093436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:31.245586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:31.381531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:31.525471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:31.701441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:31.842427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:31.987573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:32.139481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:32.283417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:32.451342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:32.675225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:32.851143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:33.016045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:33.166842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:33.321554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:33.465508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:33.610732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:33.786677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:33.939640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:34.198203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:34.426769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:34.625498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:35.066552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:35.196263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:35.332698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:35.482074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:35.595300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:35.731251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:35.851226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:35.975988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:36.106166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:36.234096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:36.354043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:36.489983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:36.619207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:36.823903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:36.967844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:37.112977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:37.269883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:37.440134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:37.636756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:37.764968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:37.900912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:38.038576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:38.185744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:38.366343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:38.509413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:38.659518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:38.820855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:38.997275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:39.149749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:39.293689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:39.413639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:39.541586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:39.667784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:39.815411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:39.967337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:40.110010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:40.253966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:40.397892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:40.555070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:40.692192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:40.860269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:41.028863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:41.172817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:41.292754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:41.428485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:41.557549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:41.677499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:41.813438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:41.949387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:42.077764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:42.213707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:42.341655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:42.493592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:42.637532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:42.786443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:42.947585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:43.083254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:43.232600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:43.369721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:43.504114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:43.649194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:43.794175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:43.930118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:44.070751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:44.218447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:44.363576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:44.811390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:45.015909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:45.185784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:45.313706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:45.446546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:45.575637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:45.703634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:45.850046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:45.983577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:46.112654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:46.249842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:46.385817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:46.529747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:46.657702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:46.806518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:46.951712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:47.087695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:47.231594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:47.359555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:47.487498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:47.623445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:47.751393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:47.887336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:48.073395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:48.222219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:48.367307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:48.503251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:48.647192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:48.800149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:48.928600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:49.120562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:49.257750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:49.385688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:49.526440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:49.664465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:49.800401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:49.936343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:50.072297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:50.224227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:50.360179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:50.509261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:50.662462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:50.798358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:50.939695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:51.133352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:51.269292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:51.413233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:51.541180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:51.685121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-03T20:47:51.821065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-08-03T20:47:58.767654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-03T20:47:59.204737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-03T20:47:59.492014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-03T20:47:59.819877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-08-03T20:47:52.320784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-03T20:47:52.813102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
014.231.712.4315.6127.02.803.060.282.295.641.043.921065.00
113.201.782.1411.2100.02.652.760.261.284.381.053.401050.00
213.162.362.6718.6101.02.803.240.302.815.681.033.171185.00
314.371.952.5016.8113.03.853.490.242.187.800.863.451480.00
413.242.592.8721.0118.02.802.690.391.824.321.042.93735.00
514.201.762.4515.2112.03.273.390.341.976.751.052.851450.00
614.391.872.4514.696.02.502.520.301.985.251.023.581290.00
714.062.152.6117.6121.02.602.510.311.255.051.063.581295.00
814.831.642.1714.097.02.802.980.291.985.201.082.851045.00
913.861.352.2716.098.02.983.150.221.857.221.013.551045.00

Last rows

alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
16813.582.582.6924.5105.01.550.840.391.548.6600000.741.80750.02
16913.404.602.8625.0112.01.980.960.271.118.5000000.671.92630.02
17012.203.032.3219.096.01.250.490.400.735.5000000.661.83510.02
17112.772.392.2819.586.01.390.510.480.649.8999990.571.63470.02
17214.162.512.4820.091.01.680.700.441.249.7000000.621.71660.02
17313.715.652.4520.595.01.680.610.521.067.7000000.641.74740.02
17413.403.912.4823.0102.01.800.750.431.417.3000000.701.56750.02
17513.274.282.2620.0120.01.590.690.431.3510.2000000.591.56835.02
17613.172.592.3720.0120.01.650.680.531.469.3000000.601.62840.02
17714.134.102.7424.596.02.050.760.561.359.2000000.611.60560.02